GAIA: A Dynamic Crowdmapping Framework Based on Hedonic Coalition Formation Games
Adedamola Adesokan, Md Sadman Siraj, Arianna Santamaria Penafiel, Eirini Eleni Tsiropoulou, Symeon Papavassiliou · 2023
Crowdsourcing has been widely employed to collect information, either at regional or global scales, about different phenomena, by engaging user communities, in order to complement or even substitute other specialized and expensive means and sources of data. In such a setting, the design of crowdsourcing models that can jointly provide appropriate rewards to the users in order to incentivize them to participate in the crowdsourcing process, while at the same time provide the necessary information to potentially various tasks (mapped to different geographical areas) announced by a requester is of high research and practical importance. In this paper, a novel dynamic crowdmapping framework is introduced, to enable the users autonomously select the geographical area, and thus corresponding task, where they will contribute their available information based on a hedonic coalition formation game. Based on the proposed hedonic coalition formation game, the requester also allocates appropriate rewards to the users considering their quality and quantity of information. The existence of a Nash-stable and individual-stable coalition formation is proven and a hedonic coalition formation algorithm is introduced to determine the stable coalition formation. The performance evaluation of the proposed framework is achieved via modeling and simulation.